Statistical Analysis for ISSRs

0. Preparation

library(tidyverse)

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## ✓ readr   1.3.1     ✓ forcats 0.5.0

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## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

setwd("/Volumes/Long_long/DAEN690Dragon/YES_CSV")
overall_summary <- read.csv("overall_summary.csv")
layer_summary <- read.csv("layer_summary.csv")

1. Statistical Analysis for the 3D Grid

1.1. Calculate hourly/daily percentage change

# calculate hourly/daily percentage change 
overall_summary <- overall_summary %>%
  mutate(hourly_pct_change = (num_of_ISSR/lag(num_of_ISSR) - 1) * 100,
         daily_pct_change = (num_of_ISSR/lag(num_of_ISSR, 24) - 1) * 100) %>%
  relocate(hourly_pct_change, .before = ceiling) %>%
  relocate(daily_pct_change, .before = ceiling)
overall_summary 

1.2. Trend of volume_of_ISSR

task2 <- overall_summary %>%
  mutate(month = floor(date/100)%%100)
task2$month<-factor(task2$month)

ggplot(data = task2, mapping = aes(x = datetime_id, y = volume_of_ISSR, fill = month)) +
  geom_bar(stat = "identity") +
  theme( axis.text.x=element_blank(), axis.ticks.x=element_blank()) +
  ylab("volume_of_ISSR (%)") + xlab("Hourly Datetime") +
  ggtitle("Trend of hourly updated volume_of_ISSR")

1.3. Trend of daily average volume_of_ISSR

task3 <- overall_summary %>%
  group_by(date_id, date) %>%
  summarise(avg_volume_of_ISSR = mean(volume_of_ISSR)) %>%
  ungroup() %>%
  mutate(pct_change = (avg_volume_of_ISSR/lag(avg_volume_of_ISSR) - 1) * 100) %>%
  mutate(month = floor(date/100)%%100)

## `summarise()` regrouping output by 'date_id' (override with `.groups` argument)

task3$date<-factor(task3$date)
task3$month<-factor(task3$month)
task3

ggplot(data = task3, mapping = aes(x = date, y = avg_volume_of_ISSR, fill = month)) +
  geom_bar(stat = "identity") +
  theme( axis.text.x=element_blank(), axis.ticks.x=element_blank()) +
  ylab("avg_volume_of_ISSR (%)") + xlab("Dates") +
  ggtitle("Trend of daily average volume_of_ISSR")

task5 <- task3 %>%
  group_by(month) %>%
  summarise(avg_volume_of_ISSR = mean(avg_volume_of_ISSR))

## `summarise()` ungrouping output (override with `.groups` argument)

task5

ggplot(data = task5, mapping = aes(x = month, y = avg_volume_of_ISSR, fill= month)) +
  geom_bar(stat = "identity") +
  ylab("avg_volume_of_ISSR (%)") + xlab("Months") +
  ggtitle("Monthly average volume_of_ISSR")

1.4. Midnight and Noon

midnight_noon <- overall_summary %>%
  filter(hour == 0 | hour == 12)
midnight_noon

midnight_noon$hour <- factor(midnight_noon$hour)
ggplot(midnight_noon, aes(x = date, y = volume_of_ISSR, col=hour)) + 
  geom_line() +
  ylab("volume_of_ISSR (%)") + xlab("Dates") +
  ggtitle("Comparasion of volume_of_ISSR between midnight and noon")

1.5. Ceilings and floors by months

overall_summary

ceiling_floor <- overall_summary %>%
  select(datetime_id, date_id, date, hour, ceiling, floor)
# ceiling_floor$datetime_id <- factor(ceiling_floor$datetime_id)
ceiling_floor

ggplot() + 
  geom_line(data = ceiling_floor, aes(x = datetime_id, y = ceiling), color = "blue") +
  geom_line(data = ceiling_floor, aes(x = datetime_id, y = floor), color = "red") +
  xlab('Dates') +
  ylab('Pressure Levels')

2. Statistical Analysis for Layers/Horizontal Grids

layer_summary

2.1. Volume of ISSRs (%) at different pressure lavels by months

month_days_df = data.frame(month = c(5,6,7,8,9,10,11,12),
                           month_days = c(15,30,31,31,30,31,30,31))

task4 <- layer_summary %>%
  mutate(month = floor(date/100)%%100) %>%
  left_join(month_days_df, by = "month") %>%
  group_by(month, pressure_level) %>%
  summarise(avg_volume_of_ISSR = sum(volume_of_ISSR)/min(month_days) ) %>%
  arrange(pressure_level)

## `summarise()` regrouping output by 'month' (override with `.groups` argument)

task4$pressure_level <- factor(task4$pressure_level)
task4$month <- factor(task4$month)
task4

ggplot(data = task4, mapping = aes(x = pressure_level, y =avg_volume_of_ISSR, fill = month)) +
  geom_bar(stat = "identity", position = "dodge") + 
  labs(title = "Volume of ISSRs (%) at different pressure levels by months",
       x = "Pressure Levels (Pa)",
       y = "Volume of ISSRs (%)") +
  theme(axis.text.x = element_text(angle = 45, face="bold"),
        axis.text.y = element_text(face="bold")) +
  theme(plot.title = element_text(size = 20, face="bold.italic"),
        axis.title.x = element_text(face="bold"),
        axis.title.y = element_text(face="bold"))